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Applications of Statistics in the Medical Area

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  1. Descriptive statistics
  2. Hypothesis testing
  3. Tests of statistical significance
  4. Multivariable methods
  5. Conclusions
  6. Bibliography

The sound practice of medicine requires the ability to use scientific evidence that is based on data and is published in the peer-reviewed medical literature. In this literature, investigators publish their findings using descriptive statistics to summarize data and inferential statistics to test hypotheses. Judgment is required in the choice of statistical tools and in the interpretation of statistical analyses. Physicians must have a basic understanding of statistics to benefit their patients by being informed and critical users of the medical literature.

[...] The decision on sample size (i.e., how many patients to study) is based on desired power, on estimates of what would be a clinically significant difference (i.e., the minimum difference in mean SBP between the groups that the investigator wishes to be able to detect), and on the known variability in SBP (its SD). If a clinically significant difference between the two groups should be at least 10 mm Hg, and a power of at least 80% is desired patients need to be randomized to each group. [...]

[...] Discriminant analysis accomplishes a similar purpose of modeling a polytomous outcome variable that is determined by several independent variables. Another multivariable analytic tool frequently found in the medical literature is Cox proportional hazards regression, in which the outcome variable is time to occurrence of a certain event. A randomized controlled trial evaluating two different treatments for lung cancer might take survival time as its outcome variable. Cox regression allows one to model the effect of the treatment on survival time, while adjusting for variables such as age, gender, and stage at diagnosis. [...]

[...] This situation would result in lack of independence of the observations within each site, requiring more advanced analytic approaches, such as hierarchical linear models or mixed models. Multivariable modeling has become deceptively easy because of the availability of powerful statistical software, which has led to its frequent misuse. Suppose that multiple logistic regression is used to study the effect of ß-blockers on mortality after myocardial infarction, and 20 deaths in a group of 200 subjects are observed. The outcome variable is vital status (alive or dead) at the end of the study. [...]

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